Deformation failure characteristics of weathered sandstone strata tunnel: a case study

Author(s):  
Ke Wang ◽  
Shuoshuo Xu ◽  
Yujian Zhong ◽  
Zhilin Han ◽  
Enlin Ma
2022 ◽  
Author(s):  
Xinyuan Zhao ◽  
Xinwang Li ◽  
Ke Yang ◽  
Lichao Cheng ◽  
Yiling Qin

Abstract The material ratio of the roadside backfill body in gob-side entry retaining determines its mechanical properties, which plays an important role in the supporting effect of the roadway surrounding rock. In this paper, a similar material modeling is used to verify the spatiotemporal law of the ground pressure in the engineering case of dense solid backfilling mining in Xingtai Mine, China. Based on that law, the theoretical requirements for the bearing performance of the roadside backfill body are proposed. Finally, a material ratio that meets the theoretical requirements is obtained by compression test, and the deformation and failure characteristics of the backfill body with this ratio are analyzed. The results show that the maximum pressure of the backfill body measured in Xingtai Mine is 5.5 MPa, which is about 40 m away from the coal face, after 40m, the pressure of the backfill body will not increase anymore. The similar simulation test also proved that the ground pressure behind the coal face increases gradually and tends to be stable during the backfilling process, which shows certain spatiotemporal characteristics. Through the proportioning experiment, it is determined that the optimal material ratio of the roadside backfill body is gangue: fly ash: cement = 10:3:1, which meets the theoretical requirement that the strength of the roadside backfill body at any position is not less than the ground pressure at that position. The research results provide a reference for the engineering practice of gob-side entry retaining in dense backfilling mining.


2021 ◽  
Author(s):  
Uthman Said

In this thesis, a maintenance evaluation and improvement methodology is presented, which makes use of maintenance data to determine failure characteristics of repairable systems and the effectiveness of maintenance policies being conducted on them. The objective is to provide a way in which maintenance data can be collected, organized, cleaned and formatted to provide information on component failures analytics, system availability and utilization so as to determine flaws in maintenance strategies. The methodology also provides context for the study of maintenance effectiveness, and synthesizes its importance within the grander scheme of maintenance optimization of repairable systems. We consider a repairable system whose failures follow a Non-Homogenous Poisson Process (NHPP) with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. We assume the effects of different preventive maintenance on the system are not identical, and estimate the parameters of the failure process as well as the effects of preventive maintenance. Ultimately, the methodology serves to guide maintenance designers in measuring the effectiveness of current maintenance policies and providing granular analysis on current failure trends to arrive at data-driven options for maintenance improvement. The proposed methodology was applied to a real case study of four AC-powered dump trucks used at an underground mine in Sudbury, Canada.


Author(s):  
Cha-Ming Shen ◽  
Tsan-Chen Chuang ◽  
Shi-Chen Lin ◽  
Lian-Fon Wen ◽  
Chen-May Huang

Abstract In this paper, we focus on how to identify non-visual failures by way of electrical analysis because some special failures cannot be observed by SEM (scanning electron microscopy) or TEM (transmission electron microscopy) even when they are precisely located by other analytical instrumentation or are symptomatic of an authentic or single suspect. The methodology described here was developed to expand the capabilities of nano-probing via C-AFM (conductive atomic forced microscopy), which can acquire detailed electrical data, and combining the technique with reasoned simulation using various mathematic models emulating all of the significant failure characteristics. Finally, a case study is presented to verify that such defect modes can be identified even when general PFA (physical failure analysis) cannot be implemented for investigating non-visual failure mechanisms.


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